{"cells":[{"cell_type":"markdown","metadata":{"id":"T2WWQiheMF7q"},"source":["# MMEditing 基础教程\n","\n","欢迎来到MMEditing！ 这是 MMEditing 的官方 Colab 教程。在本教程中，您将学习如何使用 MMEditing 中提供的 API 训练和测试恢复器。\n","\n","这是训练和测试现有模型的快速指南。如果您想基于 MMEditing 开发自己的模型并了解有关代码结构的更多信息，请参阅我们的[综合教程]()。\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"-kYw3WQ0MQry"},"source":["## 安装MMEditing\n","\n","MMEditing 可以分三步安装：\n","\n","1. 安装兼容的 PyTorch 版本（你需要使用 `nvcc -V` 检查你的 CUDA 版本）。\n","2. 安装预编译的MMCV\n","3. 克隆并安装MMEditing\n","\n","步骤如下所示："]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":321,"status":"ok","timestamp":1625140540858,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"uha_13idyl1b","outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"},"outputs":[{"name":"stdout","output_type":"stream","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions.  There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":279948,"status":"ok","timestamp":1625140820804,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"GIeIZEzZMfc0","outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l  Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl (1137.1MB)\n","\u001b[K     |███████████████████████▌        | 834.1MB 1.3MB/s eta 0:03:50tcmalloc: large alloc 1147494400 bytes == 0x56458d07a000 @  0x7fce190c6615 0x5645535bfcdc 0x56455369f52a 0x5645535c2afd 0x5645536b3fed 0x564553636988 0x5645536314ae 0x5645535c43ea 0x5645536367f0 0x5645536314ae 0x5645535c43ea 0x56455363332a 0x5645536b4e36 0x564553632853 0x5645536b4e36 0x564553632853 0x5645536b4e36 0x564553632853 0x5645536b4e36 0x5645537373e1 0x5645536976a9 0x564553602cc4 0x5645535c3559 0x5645536374f8 0x5645535c430a 0x5645536323b5 0x5645536317ad 0x5645535c43ea 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pycodestyle, mccabe, flake8, colorama, interrogate, isort, onnxruntime, pytest-runner\n","Successfully installed codecov-2.1.11 colorama-0.4.4 flake8-3.9.2 interrogate-1.4.0 isort-4.3.21 mccabe-0.6.1 onnxruntime-1.8.0 pycodestyle-2.7.0 pyflakes-2.3.1 pytest-runner-5.3.1\n","Created temporary directory: /tmp/pip-ephem-wheel-cache-hu6xvjxh\n","Created temporary directory: /tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n","  Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","    Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n","    Running command python setup.py egg_info\n","    running egg_info\n","    creating mmedit.egg-info\n","    writing mmedit.egg-info/PKG-INFO\n","    writing dependency_links to mmedit.egg-info/dependency_links.txt\n","    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requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n","  Running setup.py develop for mmedit\n","    Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n","    running develop\n","    running egg_info\n","    writing mmedit.egg-info/PKG-INFO\n","    writing dependency_links to mmedit.egg-info/dependency_links.txt\n","    writing requirements to mmedit.egg-info/requires.txt\n","    writing top-level names to mmedit.egg-info/top_level.txt\n","    reading manifest template 'MANIFEST.in'\n","    warning: no files found matching 'mmedit/VERSION'\n","    warning: no files found matching 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https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# 安装 MMEditing\n","!pip install -v -e ."]},{"cell_type":"markdown","metadata":{"id":"QgX96Sc_3PcV"},"source":["## 下载此演示所需的材料\n","在这个演示中，我们将需要一些数据和配置文件。我们将下载并放入 `./demo_files/`"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4723,"status":"ok","timestamp":1625140825508,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"-K0zFSJ-3V42","outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2021-07-01 11:59:48--  https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip      100%[===================>]  18.33M  6.00MB/s    in 3.1s    \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive:  demo_files.zip\n","   creating: demo_files/\n","  inflating: demo_files/demo_config_EDVR.py  \n","  inflating: demo_files/demo_config_BasicVSR.py  \n","   creating: demo_files/lq_sequences/\n","   creating: demo_files/lq_sequences/calendar/\n","  inflating: demo_files/lq_sequences/calendar/00000006.png  \n","  inflating: demo_files/lq_sequences/calendar/00000007.png  \n","  inflating: demo_files/lq_sequences/calendar/00000010.png  \n","  inflating: demo_files/lq_sequences/calendar/00000004.png  \n","  inflating: demo_files/lq_sequences/calendar/00000003.png  \n","  inflating: demo_files/lq_sequences/calendar/00000001.png  \n","  inflating: demo_files/lq_sequences/calendar/00000000.png  \n","  inflating: demo_files/lq_sequences/calendar/00000009.png  \n","  inflating: demo_files/lq_sequences/calendar/00000008.png  \n","  inflating: demo_files/lq_sequences/calendar/00000002.png  \n","  inflating: demo_files/lq_sequences/calendar/00000005.png  \n","   creating: demo_files/lq_sequences/city/\n","  inflating: demo_files/lq_sequences/city/00000006.png  \n","  inflating: demo_files/lq_sequences/city/00000007.png  \n","  inflating: demo_files/lq_sequences/city/00000010.png  \n","  inflating: demo_files/lq_sequences/city/00000004.png  \n","  inflating: demo_files/lq_sequences/city/00000003.png  \n","  inflating: demo_files/lq_sequences/city/00000001.png  \n","  inflating: demo_files/lq_sequences/city/00000000.png  \n","  inflating: demo_files/lq_sequences/city/00000009.png  \n","  inflating: demo_files/lq_sequences/city/00000008.png  \n","  inflating: demo_files/lq_sequences/city/00000002.png  \n","  inflating: demo_files/lq_sequences/city/00000005.png  \n","   creating: demo_files/lq_sequences/.ipynb_checkpoints/\n","   creating: demo_files/gt_images/\n","  inflating: demo_files/gt_images/bird.png  \n","  inflating: demo_files/gt_images/woman.png  \n","  inflating: demo_files/gt_images/head.png  \n","  inflating: demo_files/gt_images/baby.png  \n","  inflating: demo_files/gt_images/butterfly.png  \n","  inflating: demo_files/demo_config_SRCNN.py  \n","   creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png  \n"," extracting: demo_files/lq_images/woman.png  \n"," extracting: demo_files/lq_images/head.png  \n"," extracting: demo_files/lq_images/baby.png  \n"," extracting: demo_files/lq_images/butterfly.png  \n","   creating: demo_files/gt_sequences/\n","   creating: demo_files/gt_sequences/calendar/\n","  inflating: demo_files/gt_sequences/calendar/00000006.png  \n","  inflating: demo_files/gt_sequences/calendar/00000007.png  \n","  inflating: demo_files/gt_sequences/calendar/00000010.png  \n","  inflating: demo_files/gt_sequences/calendar/00000004.png  \n","  inflating: demo_files/gt_sequences/calendar/00000003.png  \n","  inflating: demo_files/gt_sequences/calendar/00000001.png  \n","  inflating: demo_files/gt_sequences/calendar/00000000.png  \n","  inflating: demo_files/gt_sequences/calendar/00000009.png  \n","  inflating: demo_files/gt_sequences/calendar/00000008.png  \n","  inflating: demo_files/gt_sequences/calendar/00000002.png  \n","  inflating: demo_files/gt_sequences/calendar/00000005.png  \n","   creating: demo_files/gt_sequences/city/\n","  inflating: demo_files/gt_sequences/city/00000006.png  \n","  inflating: demo_files/gt_sequences/city/00000007.png  \n","  inflating: demo_files/gt_sequences/city/00000010.png  \n","  inflating: demo_files/gt_sequences/city/00000004.png  \n","  inflating: demo_files/gt_sequences/city/00000003.png  \n","  inflating: demo_files/gt_sequences/city/00000001.png  \n","  inflating: demo_files/gt_sequences/city/00000000.png  \n","  inflating: demo_files/gt_sequences/city/00000009.png  \n","  inflating: demo_files/gt_sequences/city/00000008.png  \n","  inflating: demo_files/gt_sequences/city/00000002.png  \n","  inflating: demo_files/gt_sequences/city/00000005.png  \n","   creating: demo_files/gt_sequences/.ipynb_checkpoints/\n","   creating: demo_files/.ipynb_checkpoints/\n"]}],"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip  # 下载文件\n","!unzip demo_files  # 解压\n","\n","# 将数据复制到 data/Set5 以备后用\n","!mkdir data\n","!mkdir data/val_set5\n","!cp -r demo_files/lq_images data/val_set5/Set5_bicLRx4\n","!cp -r demo_files/gt_images data/val_set5/Set5"]},{"cell_type":"markdown","metadata":{"id":"zXGurqGKOeNE"},"source":["## 使用预训练的图像恢复器进行推理\n","您可以使用 “restoration_demo.py” 轻松地使用预训练的恢复器对单个图像进行推理。您需要的是\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `IMAGE_FILE`：输入图像的路径。\n","4. `SAVE_FILE`：您要存储输出图像的位置。\n","5. `imshow`：是否显示图片。（可选的）\n","6. `GPU_ID`：您想使用哪个 GPU。（可选的）\n","\n","获得所有这些详细信息后，您可以直接使用以下命令：\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**注:** \n","1. 配置文件位于 `./configs`。\n","2. 我们支持从 url 加载权重文件。您可以到相应页面（例如[这里](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)）获取预训练模型的url。\n","\n","---\n","\n","我们现在将使用 `SRCNN` 和 `ESRGAN` 作为示例。\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":58677,"status":"ok","timestamp":1625140884175,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"KiPvtvlqM1zb","outputId":"be7375a7-4632-4770-8383-2a8ce654b069"},"outputs":[{"name":"stdout","output_type":"stream","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png  bird_SRCNN.png\n"]}],"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# 检查图像是否已保存\n","!ls ./outputs"]},{"cell_type":"markdown","metadata":{"id":"W1DfGHu3Xcfd"},"source":["## 使用预训练的视频复原器进行推理\n","\n","MMEditing 也支持视频超分辨率方法，过程类似。您可以使用带有以下参数的 `restoration_video_demo.py`：\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `INPUT_DIR`: 包含视频帧的目录。\n","4. `OUTPUT_DIR`: 要存储输出帧的位置。\n","5. `WINDOW_SIZE`: 使用滑动窗口方法时的窗口大小（可选）。\n","6. `GPU_ID`: 您想使用哪个 GPU（可选）。\n","\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**注:** 视频超分辨率有两种不同的框架：***滑动窗口***和***循环***框架。使用 EDVR 等滑动窗口框架的方法时，需要指定 `window_size`。此值取决于您使用的模型。\n","\n","---\n","\n","我们现在将使用 `EDVR` 和 `BasicVSR` 作为示例。\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":29263,"status":"ok","timestamp":1625140913405,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"iaoE7UF5Xb2i","outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"},"outputs":[{"name":"stdout","output_type":"stream","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png  00000003.png  00000006.png  00000009.png\n","00000001.png  00000004.png  00000007.png  00000010.png\n","00000002.png  00000005.png  00000008.png\n"]}],"source":["# EDVR（滑动窗口框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR（循环框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# 检查是否保存了视频帧\n","!ls ./outputs/calendar_BasicVSR"]},{"cell_type":"markdown","metadata":{"id":"Rf3LW57qMHXb"},"source":["## 使用配置文件在预定义的数据集上进行测试\n","\n","上述演示提供了一种对单个图像或视频序列进行推理的简单方法。如果要对一组图像或序列进行推理，可以使用位于 `./configs` 中的配置文件。\n"," \n","现有的配置文件允许您对常见数据集进行推理，例如图像超分辨率中的 `Set5` 和视频超分辨率中的 `REDS4`。您可以使用以下命令：\n","\n","1. `CONFIG_FILE`: 你要使用的复原器和数据集对应的配置文件\n","2. `CHECKPOINT_FILE`: 预训练模型权重文件的路径。\n","3. `GPU_NUM`: 用于测试的 GPU 数量。\n","4. `RESULT_FILE`: 输出结果 pickle 文件的路径。（可选）\n","5. `IMAGE_SAVE_PATH`: 要存储输出图像的位置。（可选）\n","\n","```\n","# 单 GPU 测试\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# 多 GPU 测试\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","您需要做的是修改配置文件中的 `lq_folder` 和 `gt_folder`：\n","```\n","test=dict(\n","    type=val_dataset_type,\n","    lq_folder='data/val_set5/Set5_bicLRx4',\n","    gt_folder='data/val_set5/Set5',\n","    pipeline=test_pipeline,\n","    scale=scale,\n","    filename_tmpl='{}'))\n","```\n","\n","**注**: 某些数据集类型（例如 `SRREDSDataset`）需要一个注释文件来指定数据集的详细信息。更多细节请参考 `./mmedit/dataset/` 中的相应文件。\n","\n","---\n","\n","以下是 SRCNN 的命令。对于其他模型，您可以简单地更改配置文件和预训练模型的路径。\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14095,"status":"ok","timestamp":1625140927462,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"tClgIYgcbbVg","outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"},"outputs":[{"name":"stdout","output_type":"stream","text":["Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n","    return obj_cls(**args)\n","  File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n","    self.data_infos = self.load_annotations()\n","  File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n","    lq_paths = self.scan_folder(self.lq_folder)\n","  File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n","    images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n","  File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n","    for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"tools/test.py\", line 136, in <module>\n","    main()\n","  File \"tools/test.py\", line 73, in main\n","    dataset = build_dataset(cfg.data.test)\n","  File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n","    dataset = build_from_cfg(cfg, DATASETS, default_args)\n","  File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n","    raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n","    return obj_cls(**args)\n","  File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n","    self.data_infos = self.load_annotations()\n","  File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n","    lq_paths = self.scan_folder(self.lq_folder)\n","  File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n","    images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n","  File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n","    for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"./tools/test.py\", line 136, in <module>\n","    main()\n","  File \"./tools/test.py\", line 73, in main\n","    dataset = build_dataset(cfg.data.test)\n","  File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n","    dataset = build_from_cfg(cfg, DATASETS, default_args)\n","  File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n","    raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n","  File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n","    \"__main__\", mod_spec)\n","  File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n","    exec(code, run_globals)\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in <module>\n","    main()\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n","    cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"]}],"source":["# 单 GPU\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# 多 GPU\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"]},{"cell_type":"markdown","metadata":{"id":"KWKVyeEQelh3"},"source":["## 在自定义数据集上进行测试\n","\n","当您想在自定义数据集上进行测试时，除了数据集路径之外，您还需要修改 `test_dataset_type`。 \n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","这些数据集类型假定指定目录中的所有图像/序列都用于测试。文件夹结构应该是\n","```\n","| lq_root\n","  | sequence_1\n","    | 000.png\n","    | 001.png\n","    | ...\n","  | sequence_2\n","    | 000.png\n","    | ...\n","  | ...\n","| gt_root\n","  | sequence_1\n","    | 000.png\n","    | 001.png\n","    |...\n","  | sequence_2\n","    | 000.png\n","    | ...\n","  | ...\n","```\n","我们将使用 **SRCNN**、**EDVR**、**BasicVSR** 作为示例。请注意 `test_dataset_type` 和 `data['test']` 的设置。"]},{"cell_type":"markdown","metadata":{"id":"0p2rP8jV_dL1"},"source":["**SRCNN**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8729,"status":"ok","timestamp":1625140936180,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"4kEev4wVIq_L","outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"},"outputs":[{"name":"stdout","output_type":"stream","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA:     0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png  bird.png  butterfly.png  head.png  woman.png\n"]}],"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_SRCNN"]},{"cell_type":"markdown","metadata":{"id":"RONzjTTU_gem"},"source":["**EDVR**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19671,"status":"ok","timestamp":1625140955813,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"vL8WOWXY0fNJ","outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"},"outputs":[{"name":"stdout","output_type":"stream","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA:     0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar  city\n","00000000.png  00000003.png  00000006.png  00000009.png\n","00000001.png  00000004.png  00000007.png  00000010.png\n","00000002.png  00000005.png  00000008.png\n"]}],"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"]},{"cell_type":"markdown","metadata":{"id":"5Tc7F-l5_i1e"},"source":["**BasicVSR**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20220,"status":"ok","timestamp":1625140976026,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"jpW5GWC74Yvu","outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"},"outputs":[{"name":"stdout","output_type":"stream","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA:     0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar  city\n","00000000.png  00000003.png  00000006.png  00000009.png\n","00000001.png  00000004.png  00000007.png  00000010.png\n","00000002.png  00000005.png  00000008.png\n"]}],"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"]},{"cell_type":"markdown","metadata":{"id":"4DQxNL8BhI0y"},"source":["## 在预定义的数据集上训练恢复器\n","\n","MMEditing 使用分布式训练。以下命令可用于训练。如果要在我们的配置文件中指定的预定义数据集上进行训练，只需运行以下命令即可。\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","有关可选参数的更多详细信息，请参阅 `tools/train.py`。\n","\n","---\n","\n","这是一个使用 EDVR 的示例。\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9337,"status":"ok","timestamp":1625140985357,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"s-hOnSF6ItQM","outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"},"outputs":[{"name":"stdout","output_type":"stream","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May  3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n","  - GCC 7.3\n","  - C++ Version: 201402\n","  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n","  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n","  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n","  - NNPACK is enabled\n","  - CPU capability usage: AVX2\n","  - CUDA Runtime 11.0\n","  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n","  - CuDNN 8.0.4\n","  - Magma 2.5.2\n","  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n","    type='EDVR',\n","    generator=dict(\n","        type='EDVRNet',\n","        in_channels=3,\n","        out_channels=3,\n","        mid_channels=64,\n","        num_frames=5,\n","        deform_groups=8,\n","        num_blocks_extraction=5,\n","        num_blocks_reconstruction=10,\n","        center_frame_idx=2,\n","        with_tsa=False),\n","    pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n","    dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n","    dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='lq',\n","        flag='unchanged'),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='gt',\n","        flag='unchanged'),\n","    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n","    dict(\n","        type='Normalize',\n","        keys=['lq', 'gt'],\n","        mean=[0, 0, 0],\n","        std=[1, 1, 1],\n","        to_rgb=True),\n","    dict(type='PairedRandomCrop', gt_patch_size=256),\n","    dict(\n","        type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n","        direction='horizontal'),\n","    dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n","    dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n","    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n","    dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n","    dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='lq',\n","        flag='unchanged'),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='gt',\n","        flag='unchanged'),\n","    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n","    dict(\n","        type='Normalize',\n","        keys=['lq', 'gt'],\n","        mean=[0, 0, 0],\n","        std=[1, 1, 1],\n","        to_rgb=True),\n","    dict(\n","        type='Collect',\n","        keys=['lq', 'gt'],\n","        meta_keys=['lq_path', 'gt_path', 'key']),\n","    dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n","    workers_per_gpu=4,\n","    train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n","    val_dataloader=dict(samples_per_gpu=1),\n","    test_dataloader=dict(samples_per_gpu=1),\n","    train=dict(\n","        type='RepeatDataset',\n","        times=1000,\n","        dataset=dict(\n","            type=train_dataset_type,\n","            lq_folder='data/REDS/train_sharp_bicubic/X4',\n","            gt_folder='data/REDS/train_sharp',\n","            ann_file='data/REDS/meta_info_REDS_GT.txt',\n","            num_input_frames=5,\n","            pipeline=train_pipeline,\n","            scale=4,\n","            val_partition='REDS4',\n","            test_mode=False)),\n","    val=dict(\n","        type=val_dataset_type,\n","        lq_folder='data/REDS/train_sharp_bicubic/X4',\n","        gt_folder='data/REDS/train_sharp',\n","        ann_file='data/REDS/meta_info_REDS_GT.txt',\n","        num_input_frames=5,\n","        pipeline=test_pipeline,\n","        scale=4,\n","        val_partition='REDS4',\n","        test_mode=True),\n","    test=dict(\n","        type=val_dataset_type,\n","        lq_folder='data/REDS/train_sharp_bicubic/X4',\n","        gt_folder='data/REDS/train_sharp',\n","        ann_file='data/REDS/meta_info_REDS_GT.txt',\n","        num_input_frames=5,\n","        pipeline=test_pipeline,\n","        scale=4,\n","        val_partition='REDS4',\n","        test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n","    policy='CosineRestart',\n","    by_epoch=False,\n","    periods=[150000, 150000, 150000, 150000],\n","    restart_weights=[1, 0.5, 0.5, 0.5],\n","    min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n","    interval=100,\n","    hooks=[\n","        dict(type='TextLoggerHook', by_epoch=False),\n","        dict(type='TensorboardLoggerHook'),\n","        # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n","    ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n","    return obj_cls(**args)\n","  File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n","    self.data_infos = self.load_annotations()\n","  File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n","    with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"./tools/train.py\", line 145, in <module>\n","    main()\n","  File \"./tools/train.py\", line 111, in main\n","    datasets = [build_dataset(cfg.data.train)]\n","  File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n","    build_dataset(cfg['dataset'], default_args), cfg['times'])\n","  File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n","    dataset = build_from_cfg(cfg, DATASETS, default_args)\n","  File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n","    raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n","  File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n","    \"__main__\", mod_spec)\n","  File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n","    exec(code, run_globals)\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in <module>\n","    main()\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n","    cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"]}],"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"]},{"cell_type":"markdown","metadata":{"id":"b0VfQkQQjg8N"},"source":["## 在自定义数据集上训练复原器\n","\n","与您要在自己的数据集上进行测试的情况类似，您需要修改 `train_dataset_type`。您需要的数据集类型是相同的：\n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","修改数据集类型和数据路径后。一切都准备好了。"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":128384,"status":"ok","timestamp":1625141113733,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"liGEKJpbIoXZ","outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"},"outputs":[{"name":"stdout","output_type":"stream","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May  3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n","  - GCC 7.3\n","  - C++ Version: 201402\n","  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n","  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n","  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n","  - NNPACK is enabled\n","  - CPU capability usage: AVX2\n","  - CUDA Runtime 11.0\n","  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n","  - CuDNN 8.0.4\n","  - Magma 2.5.2\n","  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n","    type='BasicRestorer',\n","    generator=dict(\n","        type='SRCNN',\n","        channels=(3, 64, 32, 3),\n","        kernel_sizes=(9, 1, 5),\n","        upscale_factor=scale),\n","    pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n","    dict(\n","        type='LoadImageFromFile',\n","        io_backend='disk',\n","        key='lq',\n","        flag='unchanged'),\n","    dict(\n","        type='LoadImageFromFile',\n","        io_backend='disk',\n","        key='gt',\n","        flag='unchanged'),\n","    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n","    dict(\n","        type='Normalize',\n","        keys=['lq', 'gt'],\n","        mean=[0, 0, 0],\n","        std=[1, 1, 1],\n","        to_rgb=True),\n","    dict(type='PairedRandomCrop', gt_patch_size=128),\n","    dict(\n","        type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n","        direction='horizontal'),\n","    dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n","    dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n","    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n","    dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n","    dict(\n","        type='LoadImageFromFile',\n","        io_backend='disk',\n","        key='lq',\n","        flag='unchanged'),\n","    dict(\n","        type='LoadImageFromFile',\n","        io_backend='disk',\n","        key='gt',\n","        flag='unchanged'),\n","    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n","    dict(\n","        type='Normalize',\n","        keys=['lq', 'gt'],\n","        mean=[0, 0, 0],\n","        std=[1, 1, 1],\n","        to_rgb=True),\n","    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n","    dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n","    workers_per_gpu=8,\n","    train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n","    val_dataloader=dict(samples_per_gpu=1),\n","    test_dataloader=dict(samples_per_gpu=1),\n","    train=dict(\n","        type='RepeatDataset',\n","        times=1000,\n","        dataset=dict(\n","            type=train_dataset_type,\n","            lq_folder='./demo_files/lq_images',\n","            gt_folder='./demo_files/gt_images',\n","            pipeline=train_pipeline,\n","            scale=scale)),\n","    val=dict(\n","        type=val_dataset_type,\n","        lq_folder='./demo_files/lq_images',\n","        gt_folder='./demo_files/gt_images',\n","        pipeline=test_pipeline,\n","        scale=scale,\n","        filename_tmpl='{}'),\n","    test=dict(\n","        type=val_dataset_type,\n","        lq_folder='./demo_files/lq_images',\n","          gt_folder='./demo_files/gt_images',\n","        pipeline=test_pipeline,\n","        scale=scale,\n","        filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n","    policy='CosineRestart',\n","    by_epoch=False,\n","    periods=[250000, 250000, 250000, 250000],\n","    restart_weights=[1, 1, 1, 1],\n","    min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n","    interval=1,\n","    hooks=[\n","        dict(type='TextLoggerHook', by_epoch=False),\n","        dict(type='TensorboardLoggerHook'),\n","        # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n","    ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","INFO:mmedit:Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","2021-07-01 12:03:18,916 - mmedit - INFO - Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","INFO:mmedit:Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","2021-07-01 12:03:18,956 - mmedit - INFO - Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","INFO:mmedit:Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","2021-07-01 12:03:19,012 - mmedit - INFO - Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","INFO:mmedit:Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","2021-07-01 12:03:19,070 - mmedit - INFO - Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","INFO:mmedit:Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","2021-07-01 12:03:19,142 - mmedit - INFO - Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","INFO:mmedit:Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","2021-07-01 12:03:19,212 - mmedit - INFO - Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","INFO:mmedit:Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","2021-07-01 12:03:19,261 - mmedit - INFO - Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","INFO:mmedit:Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","2021-07-01 12:03:19,302 - 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mmedit - INFO - Iter [80/100]\tlr_generator: 2.000e-04, eta: 0:00:19, time: 0.056, data_time: 0.020, memory: 586, loss_pix: 0.0838, loss: 0.0838\n","INFO:mmedit:Iter [80/100]\tlr_generator: 2.000e-04, eta: 0:00:19, time: 0.056, data_time: 0.020, memory: 586, loss_pix: 0.0838, loss: 0.0838\n","2021-07-01 12:04:01,847 - mmedit - INFO - Iter [81/100]\tlr_generator: 2.000e-04, eta: 0:00:18, time: 0.055, data_time: 0.013, memory: 586, loss_pix: 0.0815, loss: 0.0815\n","INFO:mmedit:Iter [81/100]\tlr_generator: 2.000e-04, eta: 0:00:18, time: 0.055, data_time: 0.013, memory: 586, loss_pix: 0.0815, loss: 0.0815\n","2021-07-01 12:04:01,905 - mmedit - INFO - Iter [82/100]\tlr_generator: 2.000e-04, eta: 0:00:17, time: 0.070, data_time: 0.025, memory: 586, loss_pix: 0.0768, loss: 0.0768\n","INFO:mmedit:Iter [82/100]\tlr_generator: 2.000e-04, eta: 0:00:17, time: 0.070, data_time: 0.025, memory: 586, loss_pix: 0.0768, loss: 0.0768\n","2021-07-01 12:04:01,953 - mmedit - INFO - Iter [83/100]\tlr_generator: 2.000e-04, eta: 0:00:15, time: 0.045, data_time: 0.013, memory: 586, loss_pix: 0.0760, loss: 0.0760\n","INFO:mmedit:Iter [83/100]\tlr_generator: 2.000e-04, eta: 0:00:15, time: 0.045, data_time: 0.013, memory: 586, loss_pix: 0.0760, loss: 0.0760\n","2021-07-01 12:04:02,025 - mmedit - INFO - Iter [84/100]\tlr_generator: 2.000e-04, eta: 0:00:14, time: 0.071, data_time: 0.035, memory: 586, loss_pix: 0.0820, loss: 0.0820\n","INFO:mmedit:Iter [84/100]\tlr_generator: 2.000e-04, eta: 0:00:14, time: 0.071, data_time: 0.035, memory: 586, loss_pix: 0.0820, loss: 0.0820\n","2021-07-01 12:04:02,079 - mmedit - INFO - Iter [85/100]\tlr_generator: 2.000e-04, eta: 0:00:13, time: 0.059, data_time: 0.019, memory: 586, loss_pix: 0.0731, loss: 0.0731\n","INFO:mmedit:Iter [85/100]\tlr_generator: 2.000e-04, eta: 0:00:13, time: 0.059, data_time: 0.019, memory: 586, loss_pix: 0.0731, loss: 0.0731\n","2021-07-01 12:04:02,137 - mmedit - INFO - Iter [86/100]\tlr_generator: 2.000e-04, eta: 0:00:12, time: 0.064, data_time: 0.013, memory: 586, loss_pix: 0.0712, loss: 0.0712\n","INFO:mmedit:Iter [86/100]\tlr_generator: 2.000e-04, eta: 0:00:12, time: 0.064, data_time: 0.013, memory: 586, loss_pix: 0.0712, loss: 0.0712\n","2021-07-01 12:04:02,193 - mmedit - INFO - Iter [87/100]\tlr_generator: 2.000e-04, eta: 0:00:11, time: 0.048, data_time: 0.020, memory: 586, loss_pix: 0.0786, loss: 0.0786\n","INFO:mmedit:Iter [87/100]\tlr_generator: 2.000e-04, eta: 0:00:11, time: 0.048, data_time: 0.020, memory: 586, loss_pix: 0.0786, loss: 0.0786\n","2021-07-01 12:04:02,255 - mmedit - INFO - Iter [88/100]\tlr_generator: 2.000e-04, eta: 0:00:10, time: 0.070, data_time: 0.018, memory: 586, loss_pix: 0.0641, loss: 0.0641\n","INFO:mmedit:Iter [88/100]\tlr_generator: 2.000e-04, eta: 0:00:10, time: 0.070, data_time: 0.018, memory: 586, loss_pix: 0.0641, loss: 0.0641\n","2021-07-01 12:04:02,341 - mmedit - INFO - Iter [89/100]\tlr_generator: 2.000e-04, eta: 0:00:09, time: 0.074, data_time: 0.027, memory: 586, loss_pix: 0.0589, loss: 0.0589\n","INFO:mmedit:Iter [89/100]\tlr_generator: 2.000e-04, eta: 0:00:09, time: 0.074, data_time: 0.027, memory: 586, loss_pix: 0.0589, loss: 0.0589\n","2021-07-01 12:04:02,394 - mmedit - INFO - Iter [90/100]\tlr_generator: 2.000e-04, eta: 0:00:08, time: 0.056, data_time: 0.019, memory: 586, loss_pix: 0.0601, loss: 0.0601\n","INFO:mmedit:Iter [90/100]\tlr_generator: 2.000e-04, eta: 0:00:08, time: 0.056, data_time: 0.019, memory: 586, loss_pix: 0.0601, loss: 0.0601\n","2021-07-01 12:04:02,496 - mmedit - INFO - Iter [91/100]\tlr_generator: 2.000e-04, eta: 0:00:07, time: 0.111, data_time: 0.062, memory: 586, loss_pix: 0.0684, loss: 0.0684\n","INFO:mmedit:Iter [91/100]\tlr_generator: 2.000e-04, eta: 0:00:07, time: 0.111, data_time: 0.062, memory: 586, loss_pix: 0.0684, loss: 0.0684\n","2021-07-01 12:04:02,552 - mmedit - INFO - Iter [92/100]\tlr_generator: 2.000e-04, eta: 0:00:06, time: 0.050, data_time: 0.008, memory: 586, loss_pix: 0.0691, loss: 0.0691\n","INFO:mmedit:Iter [92/100]\tlr_generator: 2.000e-04, eta: 0:00:06, time: 0.050, data_time: 0.008, memory: 586, loss_pix: 0.0691, loss: 0.0691\n","2021-07-01 12:04:02,629 - mmedit - INFO - Iter [93/100]\tlr_generator: 2.000e-04, eta: 0:00:05, time: 0.071, data_time: 0.021, memory: 586, loss_pix: 0.0615, loss: 0.0615\n","INFO:mmedit:Iter [93/100]\tlr_generator: 2.000e-04, eta: 0:00:05, time: 0.071, data_time: 0.021, memory: 586, loss_pix: 0.0615, loss: 0.0615\n","2021-07-01 12:04:02,682 - mmedit - INFO - Iter [94/100]\tlr_generator: 2.000e-04, eta: 0:00:05, time: 0.064, data_time: 0.017, memory: 586, loss_pix: 0.0672, loss: 0.0672\n","INFO:mmedit:Iter [94/100]\tlr_generator: 2.000e-04, eta: 0:00:05, time: 0.064, data_time: 0.017, memory: 586, loss_pix: 0.0672, loss: 0.0672\n","2021-07-01 12:04:02,791 - mmedit - INFO - Iter [95/100]\tlr_generator: 2.000e-04, eta: 0:00:04, time: 0.097, data_time: 0.071, memory: 586, loss_pix: 0.0549, loss: 0.0549\n","INFO:mmedit:Iter [95/100]\tlr_generator: 2.000e-04, eta: 0:00:04, time: 0.097, data_time: 0.071, memory: 586, loss_pix: 0.0549, loss: 0.0549\n","2021-07-01 12:04:02,834 - mmedit - INFO - Iter [96/100]\tlr_generator: 2.000e-04, eta: 0:00:03, time: 0.056, data_time: 0.024, memory: 586, loss_pix: 0.0600, loss: 0.0600\n","INFO:mmedit:Iter [96/100]\tlr_generator: 2.000e-04, eta: 0:00:03, time: 0.056, data_time: 0.024, memory: 586, loss_pix: 0.0600, loss: 0.0600\n","2021-07-01 12:04:02,900 - mmedit - INFO - Iter [97/100]\tlr_generator: 2.000e-04, eta: 0:00:02, time: 0.066, data_time: 0.018, memory: 586, loss_pix: 0.0608, loss: 0.0608\n","INFO:mmedit:Iter [97/100]\tlr_generator: 2.000e-04, eta: 0:00:02, time: 0.066, data_time: 0.018, memory: 586, loss_pix: 0.0608, loss: 0.0608\n","2021-07-01 12:04:03,053 - mmedit - INFO - Iter [98/100]\tlr_generator: 2.000e-04, eta: 0:00:01, time: 0.145, data_time: 0.100, memory: 586, loss_pix: 0.0574, loss: 0.0574\n","INFO:mmedit:Iter [98/100]\tlr_generator: 2.000e-04, eta: 0:00:01, time: 0.145, data_time: 0.100, memory: 586, loss_pix: 0.0574, loss: 0.0574\n","2021-07-01 12:04:03,108 - mmedit - INFO - Iter [99/100]\tlr_generator: 2.000e-04, eta: 0:00:00, time: 0.054, data_time: 0.017, memory: 586, loss_pix: 0.0584, loss: 0.0584\n","INFO:mmedit:Iter [99/100]\tlr_generator: 2.000e-04, eta: 0:00:00, time: 0.054, data_time: 0.017, memory: 586, loss_pix: 0.0584, loss: 0.0584\n","[>>] 5/5, 0.2 task/s, elapsed: 33s, ETA:     0s\n","\n","2021-07-01 12:04:37,412 - mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"]}],"source":["# SRCNN（图像超分辨率）\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":117937,"status":"ok","timestamp":1625141554036,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"26uZ4Ak7qbC9","outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"},"outputs":[{"name":"stdout","output_type":"stream","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May  3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n","  - GCC 7.3\n","  - C++ Version: 201402\n","  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n","  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n","  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n","  - NNPACK is enabled\n","  - CPU capability usage: AVX2\n","  - CUDA Runtime 11.0\n","  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n","  - CuDNN 8.0.4\n","  - Magma 2.5.2\n","  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n","    type='EDVR',\n","    generator=dict(\n","        type='EDVRNet',\n","        in_channels=3,\n","        out_channels=3,\n","        mid_channels=64,\n","        num_frames=5,\n","        deform_groups=8,\n","        num_blocks_extraction=5,\n","        num_blocks_reconstruction=10,\n","        center_frame_idx=2,\n","        with_tsa=False),\n","    pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n","    dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n","    dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='lq',\n","        flag='unchanged'),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='gt',\n","        flag='unchanged'),\n","    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n","    dict(\n","        type='Normalize',\n","        keys=['lq', 'gt'],\n","        mean=[0, 0, 0],\n","        std=[1, 1, 1],\n","        to_rgb=True),\n","    dict(type='PairedRandomCrop', gt_patch_size=256),\n","    dict(\n","        type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n","        direction='horizontal'),\n","    dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n","    dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n","    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n","    dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n","    dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='lq',\n","        flag='unchanged'),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='gt',\n","        flag='unchanged'),\n","    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n","    dict(\n","        type='Normalize',\n","        keys=['lq', 'gt'],\n","        mean=[0, 0, 0],\n","        std=[1, 1, 1],\n","        to_rgb=True),\n","    dict(\n","        type='Collect',\n","        keys=['lq', 'gt'],\n","        meta_keys=['lq_path', 'gt_path', 'key']),\n","    dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n","    workers_per_gpu=4,\n","    train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n","    val_dataloader=dict(samples_per_gpu=1),\n","    test_dataloader=dict(samples_per_gpu=1),\n","    train=dict(\n","        type='RepeatDataset',\n","        times=1000,\n","        dataset=dict(\n","            type=train_dataset_type,\n","            lq_folder='./demo_files/lq_sequences',\n","            gt_folder='./demo_files/gt_sequences',\n","            num_input_frames=5,\n","            pipeline=train_pipeline,\n","            scale=4,\n","            test_mode=False)),\n","    val=dict(\n","        type=val_dataset_type,\n","        lq_folder='./demo_files/lq_sequences',\n","        gt_folder='./demo_files/gt_sequences',\n","        num_input_frames=5,\n","        pipeline=test_pipeline,\n","        scale=4,\n","        test_mode=True),\n","    test=dict(\n","        type=val_dataset_type,\n","        lq_folder='./demo_files/lq_sequences',\n","        gt_folder='./demo_files/gt_sequences',\n","        num_input_frames=5,\n","        pipeline=test_pipeline,\n","        scale=4,\n","        test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n","    policy='CosineRestart',\n","    by_epoch=False,\n","    periods=[150000, 150000, 150000, 150000],\n","    restart_weights=[1, 0.5, 0.5, 0.5],\n","    min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n","    interval=1,\n","    hooks=[\n","        dict(type='TextLoggerHook', by_epoch=False),\n","        dict(type='TensorboardLoggerHook'),\n","        # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n","    ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - mmedit - INFO - Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","INFO:mmedit:Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","2021-07-01 12:10:32,071 - mmedit - INFO - Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","INFO:mmedit:Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","2021-07-01 12:10:32,414 - mmedit - INFO - Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","INFO:mmedit:Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","2021-07-01 12:10:32,760 - mmedit - INFO - Iter [7/100]\tlr_generator: 4.000e-04, eta: 0:04:03, time: 0.346, data_time: 0.004, memory: 1372, loss_pix: 62958.1641, loss: 62958.1641\n","INFO:mmedit:Iter [7/100]\tlr_generator: 4.000e-04, eta: 0:04:03, time: 0.346, data_time: 0.004, memory: 1372, loss_pix: 62958.1641, loss: 62958.1641\n","2021-07-01 12:10:33,102 - mmedit - INFO - Iter [8/100]\tlr_generator: 4.000e-04, eta: 0:03:34, time: 0.343, data_time: 0.004, memory: 1372, loss_pix: 36053.0391, loss: 36053.0391\n","INFO:mmedit:Iter [8/100]\tlr_generator: 4.000e-04, eta: 0:03:34, time: 0.343, data_time: 0.004, memory: 1372, loss_pix: 36053.0391, loss: 36053.0391\n","2021-07-01 12:10:33,447 - mmedit - INFO - Iter [9/100]\tlr_generator: 4.000e-04, eta: 0:03:12, time: 0.344, data_time: 0.004, memory: 1372, loss_pix: 60384.7617, loss: 60384.7617\n","INFO:mmedit:Iter [9/100]\tlr_generator: 4.000e-04, eta: 0:03:12, time: 0.344, data_time: 0.004, memory: 1372, loss_pix: 60384.7617, loss: 60384.7617\n","2021-07-01 12:10:33,792 - mmedit - INFO - Iter [10/100]\tlr_generator: 4.000e-04, eta: 0:02:54, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 44977.9297, loss: 44977.9297\n","INFO:mmedit:Iter [10/100]\tlr_generator: 4.000e-04, eta: 0:02:54, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 44977.9297, loss: 44977.9297\n","2021-07-01 12:10:34,138 - mmedit - INFO - Iter [11/100]\tlr_generator: 4.000e-04, eta: 0:02:39, time: 0.348, data_time: 0.004, memory: 1372, loss_pix: 64897.3984, loss: 64897.3984\n","INFO:mmedit:Iter [11/100]\tlr_generator: 4.000e-04, eta: 0:02:39, time: 0.348, data_time: 0.004, memory: 1372, loss_pix: 64897.3984, loss: 64897.3984\n","2021-07-01 12:10:34,484 - mmedit - INFO - Iter [12/100]\tlr_generator: 4.000e-04, eta: 0:02:27, time: 0.344, data_time: 0.002, memory: 1372, loss_pix: 58721.3828, loss: 58721.3828\n","INFO:mmedit:Iter [12/100]\tlr_generator: 4.000e-04, eta: 0:02:27, time: 0.344, data_time: 0.002, memory: 1372, loss_pix: 58721.3828, loss: 58721.3828\n","2021-07-01 12:10:34,829 - mmedit - INFO - Iter [13/100]\tlr_generator: 4.000e-04, eta: 0:02:16, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 38433.5938, loss: 38433.5938\n","INFO:mmedit:Iter [13/100]\tlr_generator: 4.000e-04, eta: 0:02:16, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 38433.5938, loss: 38433.5938\n","2021-07-01 12:10:35,175 - mmedit - INFO - Iter [14/100]\tlr_generator: 4.000e-04, eta: 0:02:07, time: 0.346, data_time: 0.004, memory: 1372, loss_pix: 48173.8555, loss: 48173.8555\n","INFO:mmedit:Iter [14/100]\tlr_generator: 4.000e-04, eta: 0:02:07, time: 0.346, data_time: 0.004, memory: 1372, loss_pix: 48173.8555, loss: 48173.8555\n","2021-07-01 12:10:35,522 - mmedit - INFO - Iter [15/100]\tlr_generator: 4.000e-04, eta: 0:01:59, time: 0.347, data_time: 0.004, memory: 1372, loss_pix: 56551.9844, loss: 56551.9844\n","INFO:mmedit:Iter 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44153.1641\n","INFO:mmedit:Iter [18/100]\tlr_generator: 4.000e-04, eta: 0:01:40, time: 0.348, data_time: 0.004, memory: 1372, loss_pix: 44153.1641, loss: 44153.1641\n","2021-07-01 12:10:36,904 - mmedit - INFO - Iter [19/100]\tlr_generator: 4.000e-04, eta: 0:01:35, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 49195.8203, loss: 49195.8203\n","INFO:mmedit:Iter [19/100]\tlr_generator: 4.000e-04, eta: 0:01:35, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 49195.8203, loss: 49195.8203\n","2021-07-01 12:10:37,249 - mmedit - INFO - Iter [20/100]\tlr_generator: 4.000e-04, eta: 0:01:31, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 60634.4844, loss: 60634.4844\n","INFO:mmedit:Iter [20/100]\tlr_generator: 4.000e-04, eta: 0:01:31, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 60634.4844, loss: 60634.4844\n","2021-07-01 12:10:37,595 - mmedit - INFO - Iter [21/100]\tlr_generator: 4.000e-04, eta: 0:01:27, time: 0.346, data_time: 0.004, memory: 1372, loss_pix: 53779.3984, loss: 53779.3984\n","INFO:mmedit:Iter [21/100]\tlr_generator: 4.000e-04, eta: 0:01:27, time: 0.346, data_time: 0.004, memory: 1372, loss_pix: 53779.3984, loss: 53779.3984\n","2021-07-01 12:10:37,939 - mmedit - INFO - Iter [22/100]\tlr_generator: 4.000e-04, eta: 0:01:23, time: 0.344, data_time: 0.004, memory: 1372, loss_pix: 45788.3594, loss: 45788.3594\n","INFO:mmedit:Iter [22/100]\tlr_generator: 4.000e-04, eta: 0:01:23, time: 0.344, data_time: 0.004, memory: 1372, loss_pix: 45788.3594, loss: 45788.3594\n","2021-07-01 12:10:38,283 - mmedit - INFO - Iter [23/100]\tlr_generator: 4.000e-04, eta: 0:01:19, time: 0.344, data_time: 0.004, memory: 1372, loss_pix: 54939.8203, loss: 54939.8203\n","INFO:mmedit:Iter [23/100]\tlr_generator: 4.000e-04, eta: 0:01:19, time: 0.344, data_time: 0.004, memory: 1372, loss_pix: 54939.8203, loss: 54939.8203\n","2021-07-01 12:10:38,628 - mmedit - INFO - Iter [24/100]\tlr_generator: 4.000e-04, eta: 0:01:16, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 46857.5234, loss: 46857.5234\n","INFO:mmedit:Iter [24/100]\tlr_generator: 4.000e-04, eta: 0:01:16, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 46857.5234, loss: 46857.5234\n","2021-07-01 12:10:38,974 - mmedit - INFO - Iter [25/100]\tlr_generator: 4.000e-04, eta: 0:01:13, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 67588.4219, loss: 67588.4219\n","INFO:mmedit:Iter [25/100]\tlr_generator: 4.000e-04, eta: 0:01:13, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 67588.4219, loss: 67588.4219\n","2021-07-01 12:10:39,318 - mmedit - INFO - Iter [26/100]\tlr_generator: 4.000e-04, eta: 0:01:10, time: 0.344, data_time: 0.004, memory: 1372, loss_pix: 40718.7812, loss: 40718.7812\n","INFO:mmedit:Iter [26/100]\tlr_generator: 4.000e-04, eta: 0:01:10, time: 0.344, data_time: 0.004, memory: 1372, loss_pix: 40718.7812, loss: 40718.7812\n","2021-07-01 12:10:39,665 - mmedit - INFO - Iter [27/100]\tlr_generator: 4.000e-04, eta: 0:01:08, time: 0.347, data_time: 0.004, memory: 1372, loss_pix: 33732.1719, loss: 33732.1719\n","INFO:mmedit:Iter [27/100]\tlr_generator: 4.000e-04, eta: 0:01:08, time: 0.347, data_time: 0.004, memory: 1372, loss_pix: 33732.1719, loss: 33732.1719\n","2021-07-01 12:10:40,011 - mmedit - INFO - Iter [28/100]\tlr_generator: 4.000e-04, eta: 0:01:05, time: 0.346, data_time: 0.004, memory: 1372, loss_pix: 56223.7891, loss: 56223.7891\n","INFO:mmedit:Iter [28/100]\tlr_generator: 4.000e-04, eta: 0:01:05, time: 0.346, data_time: 0.004, memory: 1372, loss_pix: 56223.7891, loss: 56223.7891\n","2021-07-01 12:10:40,356 - mmedit - INFO - Iter [29/100]\tlr_generator: 4.000e-04, eta: 0:01:03, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 62915.5859, loss: 62915.5859\n","INFO:mmedit:Iter [29/100]\tlr_generator: 4.000e-04, eta: 0:01:03, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 62915.5859, loss: 62915.5859\n","2021-07-01 12:10:40,703 - mmedit - INFO - Iter [30/100]\tlr_generator: 4.000e-04, eta: 0:01:01, time: 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42591.9805, loss: 42591.9805\n","INFO:mmedit:Iter [92/100]\tlr_generator: 4.000e-04, eta: 0:00:06, time: 0.355, data_time: 0.003, memory: 1372, loss_pix: 42591.9805, loss: 42591.9805\n","2021-07-01 12:11:29,628 - mmedit - INFO - Iter [93/100]\tlr_generator: 4.000e-04, eta: 0:00:05, time: 0.362, data_time: 0.004, memory: 1372, loss_pix: 44414.8047, loss: 44414.8047\n","INFO:mmedit:Iter [93/100]\tlr_generator: 4.000e-04, eta: 0:00:05, time: 0.362, data_time: 0.004, memory: 1372, loss_pix: 44414.8047, loss: 44414.8047\n","2021-07-01 12:11:29,989 - mmedit - INFO - Iter [94/100]\tlr_generator: 4.000e-04, eta: 0:00:04, time: 0.360, data_time: 0.003, memory: 1372, loss_pix: 55887.0938, loss: 55887.0938\n","INFO:mmedit:Iter [94/100]\tlr_generator: 4.000e-04, eta: 0:00:04, time: 0.360, data_time: 0.003, memory: 1372, loss_pix: 55887.0938, loss: 55887.0938\n","2021-07-01 12:11:30,345 - mmedit - INFO - Iter [95/100]\tlr_generator: 4.000e-04, eta: 0:00:03, time: 0.357, data_time: 0.004, memory: 1372, loss_pix: 50413.8594, loss: 50413.8594\n","INFO:mmedit:Iter [95/100]\tlr_generator: 4.000e-04, eta: 0:00:03, time: 0.357, data_time: 0.004, memory: 1372, loss_pix: 50413.8594, loss: 50413.8594\n","2021-07-01 12:11:30,704 - mmedit - INFO - Iter [96/100]\tlr_generator: 4.000e-04, eta: 0:00:03, time: 0.357, data_time: 0.003, memory: 1372, loss_pix: 56848.2617, loss: 56848.2617\n","INFO:mmedit:Iter [96/100]\tlr_generator: 4.000e-04, eta: 0:00:03, time: 0.357, data_time: 0.003, memory: 1372, loss_pix: 56848.2617, loss: 56848.2617\n","2021-07-01 12:11:31,067 - mmedit - INFO - Iter [97/100]\tlr_generator: 4.000e-04, eta: 0:00:02, time: 0.364, data_time: 0.004, memory: 1372, loss_pix: 48228.6172, loss: 48228.6172\n","INFO:mmedit:Iter [97/100]\tlr_generator: 4.000e-04, eta: 0:00:02, time: 0.364, data_time: 0.004, memory: 1372, loss_pix: 48228.6172, loss: 48228.6172\n","2021-07-01 12:11:31,425 - mmedit - INFO - Iter [98/100]\tlr_generator: 4.000e-04, eta: 0:00:01, time: 0.359, data_time: 0.003, memory: 1372, loss_pix: 46352.1172, loss: 46352.1172\n","INFO:mmedit:Iter [98/100]\tlr_generator: 4.000e-04, eta: 0:00:01, time: 0.359, data_time: 0.003, memory: 1372, loss_pix: 46352.1172, loss: 46352.1172\n","2021-07-01 12:11:31,782 - mmedit - INFO - Iter [99/100]\tlr_generator: 4.000e-04, eta: 0:00:00, time: 0.357, data_time: 0.004, memory: 1372, loss_pix: 56967.3750, loss: 56967.3750\n","INFO:mmedit:Iter [99/100]\tlr_generator: 4.000e-04, eta: 0:00:00, time: 0.357, data_time: 0.004, memory: 1372, loss_pix: 56967.3750, loss: 56967.3750\n","[>>] 22/22, 0.9 task/s, elapsed: 26s, ETA:     0s\n","\n","2021-07-01 12:11:58,494 - mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:11:58,598 - mmedit - INFO - Iter(val) [100]\tPSNR: 21.3930, SSIM: 0.5684\n","INFO:mmedit:Iter(val) [100]\tPSNR: 21.3930, SSIM: 0.5684\n"]}],"source":["# EDVR（视频超分辨率-滑动窗口）\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":197033,"status":"ok","timestamp":1625141428032,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"},"user_tz":-480},"id":"_RdqmlT6qgt2","outputId":"b951b426-e06c-4f31-db01-449333eab333"},"outputs":[{"name":"stdout","output_type":"stream","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May  3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n","  - GCC 7.3\n","  - C++ Version: 201402\n","  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n","  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n","  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n","  - NNPACK is enabled\n","  - CPU capability usage: AVX2\n","  - CUDA Runtime 11.0\n","  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n","  - CuDNN 8.0.4\n","  - Magma 2.5.2\n","  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n","    type='BasicVSR',\n","    generator=dict(\n","        type='BasicVSRNet',\n","        mid_channels=64,\n","        num_blocks=30,\n","        spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n","        'basicvsr/spynet_20210409-c6c1bd09.pth'),\n","    pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n","    dict(type='GenerateSegmentIndices', interval_list=[1]),\n","    dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='lq',\n","        channel_order='rgb'),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='gt',\n","        channel_order='rgb'),\n","    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n","    dict(type='PairedRandomCrop', gt_patch_size=256),\n","    dict(\n","        type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n","        direction='horizontal'),\n","    dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n","    dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n","    dict(type='FramesToTensor', keys=['lq', 'gt']),\n","    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n","    dict(type='GenerateSegmentIndices', interval_list=[1]),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='lq',\n","        channel_order='rgb'),\n","    dict(\n","        type='LoadImageFromFileList',\n","        io_backend='disk',\n","        key='gt',\n","        channel_order='rgb'),\n","    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n","    dict(type='FramesToTensor', keys=['lq', 'gt']),\n","    dict(\n","        type='Collect',\n","        keys=['lq', 'gt'],\n","        meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n","    workers_per_gpu=6,\n","    train_dataloader=dict(samples_per_gpu=4, drop_last=True),  # 2 gpus\n","    val_dataloader=dict(samples_per_gpu=1),\n","    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n","    # train\n","    train=dict(\n","        type='RepeatDataset',\n","        times=1000,\n","        dataset=dict(\n","            type=train_dataset_type,\n","            lq_folder='./demo_files/lq_sequences',\n","            gt_folder='./demo_files/gt_sequences',\n","            num_input_frames=5,\n","            pipeline=train_pipeline,\n","            scale=4,\n","            test_mode=False)),\n","    # val\n","    val=dict(\n","        type=val_dataset_type,\n","        lq_folder='./demo_files/lq_sequences',\n","        gt_folder='./demo_files/gt_sequences',\n","        pipeline=test_pipeline,\n","        scale=4,\n","        test_mode=True),\n","    # test\n","    test=dict(\n","        type=val_dataset_type,\n","        lq_folder='./demo_files/lq_sequences',\n","        gt_folder='./demo_files/gt_sequences',\n","        pipeline=test_pipeline,\n","        scale=4,\n","        test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n","    generator=dict(\n","        type='Adam',\n","        lr=2e-4,\n","        betas=(0.9, 0.99),\n","        paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n","    policy='CosineRestart',\n","    by_epoch=False,\n","    periods=[300000],\n","    restart_weights=[1],\n","    min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n","    interval=1,\n","    hooks=[\n","        dict(type='TextLoggerHook', by_epoch=False),\n","        # dict(type='TensorboardLoggerHook'),\n","    ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - mmedit - INFO - Iter [5/100]\tlr_generator: 2.500e-05, eta: 0:09:22, time: 0.889, data_time: 0.003, memory: 3518, loss_pix: 0.0796, loss: 0.0796\n","2021-07-01 12:07:18,712 - mmedit - INFO - Iter [6/100]\tlr_generator: 2.500e-05, eta: 0:07:57, time: 0.884, data_time: 0.004, memory: 3518, loss_pix: 0.0680, loss: 0.0680\n","2021-07-01 12:07:19,594 - mmedit - INFO - Iter [7/100]\tlr_generator: 2.500e-05, eta: 0:06:56, time: 0.882, data_time: 0.003, memory: 3518, loss_pix: 0.0607, loss: 0.0607\n","2021-07-01 12:07:20,481 - mmedit - INFO - Iter [8/100]\tlr_generator: 2.500e-05, eta: 0:06:10, time: 0.887, data_time: 0.003, memory: 3518, loss_pix: 0.0598, loss: 0.0598\n","2021-07-01 12:07:21,361 - mmedit - INFO - Iter [9/100]\tlr_generator: 2.500e-05, eta: 0:05:35, time: 0.880, data_time: 0.003, memory: 3518, loss_pix: 0.0664, loss: 0.0664\n","2021-07-01 12:07:22,274 - mmedit - INFO - Iter [10/100]\tlr_generator: 2.500e-05, eta: 0:05:06, time: 0.913, data_time: 0.003, memory: 3518, loss_pix: 0.0687, loss: 0.0687\n","2021-07-01 12:07:23,161 - mmedit - INFO - Iter [11/100]\tlr_generator: 2.500e-05, eta: 0:04:42, time: 0.887, data_time: 0.005, memory: 3518, loss_pix: 0.0771, loss: 0.0771\n","2021-07-01 12:07:24,058 - mmedit - INFO - Iter [12/100]\tlr_generator: 2.500e-05, eta: 0:04:22, time: 0.897, data_time: 0.003, memory: 3518, loss_pix: 0.0521, loss: 0.0521\n","2021-07-01 12:07:24,944 - mmedit - INFO - Iter [13/100]\tlr_generator: 2.500e-05, eta: 0:04:05, time: 0.887, data_time: 0.003, memory: 3518, loss_pix: 0.0675, loss: 0.0675\n","2021-07-01 12:07:25,835 - mmedit - INFO - Iter [14/100]\tlr_generator: 2.500e-05, eta: 0:03:51, time: 0.891, data_time: 0.003, memory: 3518, loss_pix: 0.0515, loss: 0.0515\n","2021-07-01 12:07:26,723 - mmedit - INFO - Iter [15/100]\tlr_generator: 2.500e-05, eta: 0:03:38, time: 0.887, data_time: 0.003, memory: 3518, loss_pix: 0.0674, loss: 0.0674\n","2021-07-01 12:07:27,609 - mmedit - INFO - Iter [16/100]\tlr_generator: 2.500e-05, eta: 0:03:26, time: 0.886, data_time: 0.003, memory: 3518, loss_pix: 0.0579, loss: 0.0579\n","2021-07-01 12:07:28,498 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"]}],"source":["# BasicVSR（视频超分辨率 - 循环）\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "]},{"cell_type":"markdown","metadata":{"id":"QT0zwBFt7J13"},"source":["**本教程到此结束。有关更高级的用法，请参阅我们的[综合教程]()。享受使用 MMEditing 的乐趣！**"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"name":"restorer_basic_tutorial.ipynb","provenance":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"nbformat":4,"nbformat_minor":2}
